Thermomechanical characterization of functionally stabilized nickel-titanium-copper shape memory alloy
Bibliographic record
Abstract
Abstract Shape memory alloy hybrid composites have promise in realizing the 21st century goal of morphing structures. There is considerable work to be done in the development of characterization and modeling techniques for these materials. The proposed characterization methodology adapts existing standards to include previously omitted factors required for the numerical modelling of shape memory alloys and their integration into end-use applications. A nickel-titanium-copper (NiTiCu) shape memory alloy is characterized using these methods and then numerically modelled. Samples’ mechanical behaviour is shown to stabilize after 43 cycles of mechanical loading. Thermomechanical properties measured before and after stabilization are shown to vary inconsistently by up to 72%, demonstrating the need for stabilization for accurate thermomechanical characterizations and consistency in end-use applications. Physical experiments are numerically replicated in Abaqus\Standard using the measured properties. Sufficient correlation is shown for the design of shape memory alloy hybrid composites. The result of this work is a comprehensive thermomechanical characterization approach for shape memory alloys which can be used to develop morphing SMA hybrid composite structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".